The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Peter James

Peter James

Professor

Peter James

A probabilistic treatment of the missing spot problem in 2D gel electrophoresis experiments

Author

  • Morten Krogh
  • Celine Fernandez
  • Maria Teilum
  • Sofia Bengtsson
  • Peter James

Summary, in English

Two-dimensional SIDS-PAGE gel electrophoresis using post-run staining is widely used to measure the abundances of thousands of protein spots simultaneously. Usually, the protein abundances of two or more biological groups are compared using biological and technical replicates. After gel separation and staining, the spots are detected, spot volumes are quantified, and spots are matched across gels. There are almost always many missing values in the resulting data set. The missing values arise either because the corresponding proteins have very low abundances (or are absent) or because of experimental errors such as incomplete/over focusing in the first dimension or varying run times in the second dimension as well as faulty spot detection and matching. In this study, we show that the probability for a spot to be missing can be modeled by a logistic regression function of the logarithm of the volume. Furthermore, we present an algorithm that takes a set of gels with technical and biological replicates as input and estimates the average protein abundances in the biological groups from the number of missing spots and measured volumes of the present spots using a maximum likelihood approach. Confidence intervals for abundances and p-values for differential expression between two groups are calculated using bootstrap sampling. The algorithm is compared to two standard approaches, one that discards missing values and one that sets all missing values to zero. We have evaluated this approach in two different gel data sets of different biological origin. An F-program, implementing the algorithm, is freely available at httP://bioinfo.thep.lu.se/MissingValues2Dgels.html.

Department/s

  • Computational Biology and Biological Physics - Has been reorganised
  • Molecular Endocrinology
  • Section IV
  • Department of Immunotechnology

Publishing year

2007

Language

English

Pages

3335-3343

Publication/Series

Journal of Proteome Research

Volume

6

Issue

8

Document type

Journal article

Publisher

The American Chemical Society (ACS)

Topic

  • Neurology
  • Endocrinology and Diabetes

Keywords

  • missing values
  • maximum likelihood
  • 2D-PAGE

Status

Published

Research group

  • Molecular Endocrinology

ISBN/ISSN/Other

  • ISSN: 1535-3893